from gekko import GEKKO import numpy as np import matplotlib.pyplot as plt m = GEKKO() m.time = np.linspace(0,20,41) # Parameters mass = 500 b = m.Param(value=50) K = m.Param(value=0.8) # Manipulated variable p = m.MV(value=0, lb=0, ub=100) p.STATUS = 1 # allow optimizer to change p.DCOST = 0.1 # smooth out gas pedal movement p.DMAX = 20 # slow down change of gas pedal # Controlled Variable v = m.CV(value=0) v.STATUS = 1 # add the SP to the objective m.options.CV_TYPE = 2 # squared error v.SP = 40 # set point v.TR_INIT = 1 # set point trajectory v.TAU = 5 # time constant of trajectory # Process model m.Equation(mass*v.dt() == -v*b + K*b*p) m.options.IMODE = 6 # control m.solve(disp=False,GUI=True) # get additional solution information import json with open(m.path+'//results.json') as f: results = json.load(f) plt.figure() plt.subplot(2,1,1) plt.plot(m.time,p.value,'b-',label='MV Optimized') plt.legend() plt.ylabel('Input') plt.subplot(2,1,2) plt.plot(m.time,results['v1.tr'],'k-',label='Reference Trajectory') plt.plot(m.time,v.value,'r--',label='CV Response') plt.ylabel('Output') plt.xlabel('Time') plt.legend(loc='best') plt.show()